Neural Network Architectures

This repository provides an overview of various neural network architectures, including Self-Organizing Maps (SOM), Boltzmann Machines, and Autoencoders. Each type of neural network has its unique structure and application areas. Below is a detailed description of each architecture.

Table of Contents

• Self-Organizing Maps (SOM)
• Boltzmann Machines
• Autoencoders

Self-Organizing Maps (SOM)

Self-Organizing Maps (SOMs) are a type of unsupervised learning network used for clustering and visualizing high-dimensional data.

Key Features:

• Topology Preservation: Maintain the spatial relationships of input data.
• Competitive Learning: Neurons compete to respond to a subset of the input data.

Applications:

• Clustering
• Data visualization
• Dimensionality reduction

Boltzmann Machines

Boltzmann Machines are stochastic neural networks capable of learning internal representations. They consist of visible and hidden units with symmetric connections.

Key Features:

• Energy-Based Model: The network assigns an energy to each configuration of the variables.
• Restricted Boltzmann Machines (RBM): A variant with a restricted topology to facilitate learning.

Applications:

• Dimensionality reduction
• Collaborative filtering
• Feature learning

Autoencoders

Autoencoders are unsupervised neural networks used for learning efficient codings of input data. They consist of an encoder and a decoder.

Key Features:

• Encoder: Maps input data to a lower-dimensional latent space.
• Decoder: Reconstructs the input data from the latent representation.
• Loss Function: Measures the difference between the input and its reconstruction (e.g., Mean Squared Error).

Applications:

• Data denoising
• Dimensionality reduction
• Anomaly detection

References

• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
• Neural Networks and Deep Learning by Michael Nielsen
• MIT Deep Learning for Self-Driving Cars